CN114460087A - Welding spot defect detection system and method based on machine vision - Google Patents
Welding spot defect detection system and method based on machine vision Download PDFInfo
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- 238000003466 welding Methods 0.000 title claims abstract description 65
- 238000001514 detection method Methods 0.000 title claims abstract description 50
- 230000007547 defect Effects 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 18
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- 229910000679 solder Inorganic materials 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 3
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- 238000004519 manufacturing process Methods 0.000 abstract description 7
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- 238000005457 optimization Methods 0.000 description 3
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- 230000007613 environmental effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
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- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G06T7/0002—Inspection of images, e.g. flaw detection
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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Abstract
The invention provides a welding spot defect detection system based on machine vision, which comprises: the control module confirms the state of hardware, transmits data and issues a control command; the robot module is connected with the control module and executes a preset motion script according to an instruction issued by the control module; the image acquisition and control module and the robot module are respectively connected, image acquisition parameters are set according to instructions of the control module, and the image acquisition pose is adjusted according to the motion trail of the robot module; the vision algorithm module is connected with the control module and the image acquisition module, receives data of the image acquisition module and detects whether the body-in-white reaches a specified station and welding points of the body-in-white; and outputs data to the control module. According to the invention, the robot module, the image acquisition module and the control module are adopted to complete automatic design, so that the automation level of a production workshop is improved, and the labor cost is reduced.
Description
Technical Field
The invention relates to the technical field of automobile manufacturing, in particular to a welding spot defect detection system and method based on machine vision.
Background
The quality detection of the welding points of the automobile body is one of important parts of the detection of the automobile body, and currently, most automobile manufacturers judge whether the positions of the welding points are correct and the quality of the welding points reaches the standard by means of visual observation and finger touch by adopting a manual detection method. Generally, more than 2 skilled workers need to be configured for detecting welding points of the vehicle body on each production line, and meanwhile, a sampling inspection needs to be arranged to ensure that the efficiency reaches the standard.
The working method automation level of the current vehicle body welding spot quality detection is lower, consumes higher human cost, and moreover, the efficiency, the precision and the accuracy of manual detection have considerable uncertainty, are influenced by factors such as the skill level of workers and the working state, and can bring potential risks to the whole production line.
The invention of China with the application number of 202010248490.2 retrieved from the prior art discloses a visual inspection method and a system for 3D defect inspection, wherein the method is that a defect inspection visual controller controls a 3D camera to take images after receiving a product in-place signal of a motion controller, and generates a 3D point cloud image of a product to be inspected; the defect detection visual controller analyzes the 3D point cloud image of the product to be detected to generate image analysis data; the defect detection visual controller carries out statistical classification on the image analysis data and judges the detection result of the product to be detected; and the defect detection vision controller displays the detection result and the analysis data and feeds the detection result back to the motion controller.
Through the search of the prior art, the Chinese invention with the application number of 202110051630.1 discloses a quick high-precision defect detection method based on visual images, which comprises the following steps: positioning a reference object and acquiring an image, creating a matching template and determining a detection area, positioning an object to be detected and acquiring an image, comparing the image of the reference area with the image of the area to be detected, extracting an abnormal area, extracting the characteristics of the abnormal area and screening.
The two invention patents do not solve the technical problem of how to accurately detect the welding points of the car body.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a welding spot defect detection system and method based on machine vision.
According to an aspect of the present invention, there is provided a welding spot defect detecting system based on machine vision, including:
the control module confirms the state of hardware, transmits data and issues a control instruction;
the robot module is connected with the control module and executes a preset motion script according to an instruction issued by the control module;
the image acquisition module is respectively connected with the control module and the robot module, sets image acquisition parameters according to instructions of the control module, and adjusts the image acquisition pose according to the motion track of the robot module;
the visual algorithm module is connected with the control module and the image acquisition module, receives data of the image acquisition module and detects whether the body-in-white arrives at a designated station or not and welding spot information of the body-in-white; and outputs data to the control module.
Preferably, the state of the hardware comprises: the position state of the rolling machine, the motion state of the robot, the illumination and image acquisition state;
the transferring data comprises: image data, status data of the hardware, and image detection results;
the control instructions include: a robot control instruction and a camera control instruction; the robot control instruction comprises movement control of the robot; the camera control instruction comprises brightness control of the shooting light source, parameter setting of the camera and shooting setting.
Preferably, the robot module is connected with the image acquisition module, and the image acquisition module moves to an appointed shooting pose through the robot module, traverses the vehicle body, and acquires all welding spot images.
Preferably, the image acquisition module comprises:
a camera that captures the vehicle body image,
a light source surrounding the camera providing a photographic light source thereto;
and in different photographing positions, the image acquisition module sets illumination parameters and camera parameters according to the state instructions transmitted by the control module, and photographs images and transmits the images to the visual algorithm module.
Preferably, the image acquisition module comprises:
a non-standard camera mount having an industrial camera centrally disposed therein;
an annular light source connected to the non-standard camera mount to provide a light source for the industrial camera.
Preferably, the non-standard camera stand comprises:
a first circular table, which is positioned at the top,
the second circular table is positioned on the lower layer of the first circular table, the second circular table and the first circular table are mutually supported and fixed by at least one upright post, and the industrial camera is arranged at the center of the second circular table;
the annular light source is positioned on the lower layer of the second circular truncated cone, and the annular light source and the lower layer of the second circular truncated cone are mutually supported and fixed by at least one support.
Preferably, the detecting whether the body-in-white reaches the station comprises:
the control module confirms whether the body-in-white arrives at a designated station according to the working state of the rolling machine;
the control module issues a moving instruction to the robot module, so that the camera reaches a specified position, and a light source is started to collect an image;
the vision algorithm module identifies the vehicle body and confirms whether the vehicle body meets the requirement of detection pose;
and if the pose meets the detection requirement, confirming that the white vehicle body station is confirmed.
According to a second aspect of the present invention, there is provided a method for detecting solder joint defects based on machine vision,
collecting image data;
processing the image data to perform welding spot identification;
and detecting the identified welding spots for defects.
Preferably, the processing the image data for solder joint recognition includes:
preprocessing the image data with median filtering and edge sharpening,
the weld point edges in the preprocessed image are extracted by canny filtering,
detecting template welding spots by using Hough transform;
and (4) screening holes and false welding points by combining the gray value of the area and the neighborhood condition of the outline to obtain the identified welding points.
Preferably, the detecting the defects of the identified welding spots comprises:
extracting shape features and gray scale features of the identified welding spots;
calculating the position information and the shape information of the welding spot based on the shape characteristic and the gray characteristic, and obtaining a calculation result;
and judging whether defects exist according to the calculation result.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the robot module, the image acquisition module and the control module are adopted to complete automatic design, so that the automation level of a production workshop is improved, and the labor cost is reduced;
according to the invention, the image acquisition module and the visual algorithm module are utilized, so that the detection efficiency of the welding spot of the vehicle body is improved, and the production beat is accelerated; the visual algorithm module improves the detection precision and stability and reduces the probability of missed identification and false identification.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of a welding spot defect detecting system based on machine vision according to an embodiment of the present invention;
FIG. 2 is a schematic view of an angle of an image capture module according to an embodiment of the present invention;
FIG. 3 is a schematic view of another angle of an image capture module according to an embodiment of the present invention;
fig. 4 is a schematic application diagram of a welding spot defect detection method based on machine vision according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in FIG. 1, the present invention provides a schematic diagram of a welding spot defect detecting system based on machine vision, including: the robot comprises a control module, a robot module, an image acquisition module and a visual algorithm module. The control module confirms the state of the hardware, transmits data and issues a control instruction; the robot module is connected with the control module and executes a preset motion script according to an instruction issued by the control module; the image acquisition and control module and the robot module are respectively connected, image acquisition parameters are set according to instructions of the control module, and the image acquisition pose is adjusted according to the motion trail of the robot module; the vision algorithm module is connected with the control module and the image acquisition module, receives data of the image acquisition module and detects whether the body-in-white arrives at a designated station and welding spots of the body-in-white; and outputs data to the control module.
Further optimization based on the above embodiment provides a preferred embodiment. In this embodiment, the states of the hardware include: the position state of the rolling machine, the motion state of the robot, the illumination and the image acquisition state. The position state of the rolling machine reflects whether the body-in-white is conveyed to a detection area of the system, when the rolling machine is not conveyed or is in a conveying state, the rolling machine returns to an un-ready state, and when the body-in-white is conveyed to the detection area, the rolling machine returns to a ready state; the roller bed is used for conveying the body-in-white. Returning to a ready state when the execution of the moving instruction of the robot is finished; and after the camera finishes shooting and image transmission under illumination, returning to the ready state.
The data transfer includes: image data, hardware status data, and image detection results; the control instructions include: a robot control instruction and a camera control instruction; the robot control instruction comprises movement control of the robot; the camera control instruction comprises brightness control of the shooting light source, parameter setting of the camera and shooting setting.
The robot module is connected with the image acquisition module, the image acquisition module moves to an appointed shooting pose through the robot module, the vehicle body is traversed, and all welding spot images are acquired.
The image acquisition module includes: the vehicle body image shooting device comprises a camera and a light source, wherein the camera shoots a vehicle body image, and the light source surrounds the camera and provides shooting light sources for the camera. And in different photographing positions, the image acquisition module sets illumination parameters and camera parameters according to the state instructions transmitted by the control module, and photographs images and transmits the images to the visual algorithm module.
Further, as shown in fig. 2 and fig. 3, a schematic diagram of an image capturing module according to an embodiment of the present invention is provided. The image acquisition module includes: the camera comprises a non-standard camera support and an annular light source, wherein an industrial camera is arranged in the center of the non-standard camera support; the annular light source is connected with the non-standard camera bracket and provides a light source for the industrial camera. Specifically, the method comprises the following steps: the light source device comprises a first circular table, a second circular table and a light source ring. The first round platform is positioned at the top, the second round platform is positioned at the lower layer of the first round platform and is mutually supported and fixed with the first round platform by using at least one upright post, and the industrial camera is arranged at the center of the second round platform; the light source ring is positioned at the lower layer of the second circular table and is mutually supported and fixed with the second circular table by using at least one support. Enough space is reserved in the annular light source, various cameras can be placed in the annular light source, and the connecting flange is suitable for various cameras.
In order to better perform defect detection, the present invention provides a preferred embodiment. In this embodiment, before entering defect detection stage, whether the body-in-white reaches the station needs to be detected, including:
s10, the control module confirms whether the body-in-white arrives at the designated station according to the working state of the rolling machine;
s20, the control module sends a moving instruction to the robot module to enable the camera to reach the designated position and start the light source to collect images;
s30, the vision algorithm module identifies the vehicle body and confirms whether the vehicle body meets the requirement of the detection pose;
and S40, if the pose meets the detection requirement, confirming that the white body station confirmation is completed.
The defect detection process can be carried out only by the pose detection.
The present invention provides another embodiment based on the same concept of the above-described embodiments. A welding spot defect detection method based on machine vision comprises the following steps:
s100, collecting image data;
s200, processing the image data to perform welding spot identification;
s300, detecting defects of the identified welding spots.
Further optimization based on the above embodiment provides a preferred embodiment. Processing image data for solder joint identification, comprising:
s201, preprocessing image data by adopting median filtering and edge sharpening,
s202, extracting the edge of the welding point in the preprocessed image through canny filtering,
s203, detecting welding spots by using Hough transform;
s204, holes and false welding points are screened out by combining the gray value of the area where the welding points are located and the neighborhood condition of the outline of the area. The welding points are different from the holes and the false welding points in gray distribution, and can be distinguished by utilizing the gray values of the interior and the neighborhood of the identified region.
Further optimization based on the above embodiment provides a preferred embodiment. Detecting defects of the identified welding spots, comprising:
s301, extracting shape features and gray features of the identified welding spots;
s302, calculating the position information and the shape information of the welding spot based on the shape feature and the gray feature, and obtaining a calculation result. Specifically, the position and the radius of the center of the minimum circumscribed circle are calculated to obtain the position and the size of the welding spot.
In the embodiment, manual welding spot detection is replaced by a machine vision detection algorithm, and compared with the traditional detection method, each production line is only provided with one welding spot detection workstation without additional workers; through testing, for a single welding spot picture, the detection speed of the algorithm reaches the millisecond level; the position error of the welding spot is less than 0.1mm, the radius error is less than 5 percent, and the working standard of the welding spot detection of the car body is achieved; the visual algorithm module in the embodiment filters the interference of environmental factors such as illumination, noise and the like, and has high stability.
As shown in fig. 4, a preferred embodiment of the present invention is provided. An application schematic diagram of a welding spot defect detection method based on machine vision comprises a white body station, a robot, a camera installed on the robot, a robot electric appliance cabinet and a desktop.
S0: confirming the in-place of the body in white;
s1: the user desktop sends a preset pose requirement to the robot electrical cabinet;
s2: the robot electrical cabinet sends a control instruction to the robot, and the robot moves to a defect detection position;
s3: the robot sends the acquired pose information of the body-in-white to the robot electrical cabinet;
s4: the robot electrical cabinet confirms the position of the vehicle body through a vision algorithm and sends in-place confirmation information to a user desktop;
s5: the desktop of the user sends a shooting instruction to the camera;
s6: the camera sends the acquired picture information to a user desktop computer, and the user desktop computer detects welding spots and defects by using a data amount algorithm;
s7: and the user desktop outputs and stores the calculation result.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The above-described preferred features may be used in any combination without conflict with each other.
Claims (10)
1. A welding spot defect detecting system based on machine vision is characterized by comprising:
the control module confirms the state of hardware, transmits data and issues a control command;
the robot module is connected with the control module and executes a preset motion script according to an instruction issued by the control module;
the image acquisition module is respectively connected with the control module and the robot module, sets image acquisition parameters according to instructions of the control module, and adjusts the image acquisition pose according to the motion track of the robot module;
the visual algorithm module is connected with the control module and the image acquisition module, receives data of the image acquisition module and detects whether the body-in-white arrives at a designated station or not and welding spot information of the body-in-white; and outputs data to the control module.
2. The machine-vision-based solder joint defect detection system of claim 1, wherein the state of the hardware comprises: the position state of the rolling machine, the motion state of the robot, the illumination and image acquisition state;
the transferring data comprises: image data, status data of the hardware, and image detection results;
the control instructions include: a robot control instruction and a camera control instruction; the robot control instruction comprises movement control of the robot; the camera control instruction comprises brightness control of the shooting light source, parameter setting of the camera and shooting setting.
3. The welding spot defect detecting system based on machine vision according to claim 1, characterized in that the robot module is connected with the image acquisition module, the image acquisition module moves to a designated shooting pose through the robot module, traverses a vehicle body, and acquires all welding spot images.
4. The machine-vision-based weld spot defect detection system of claim 1, wherein the image acquisition module comprises:
a camera that captures the vehicle body image,
a light source surrounding the camera providing a photographic light source thereto;
and in different photographing positions, the image acquisition module sets illumination parameters and camera parameters according to the state instructions transmitted by the control module, and photographs images and transmits the images to the visual algorithm module.
5. The machine-vision-based weld spot defect detection system of claim 4, wherein the image acquisition module comprises:
a non-standard camera mount having an industrial camera centrally disposed therein;
an annular light source connected to the non-standard camera mount to provide a light source for the industrial camera.
6. The machine-vision-based solder joint defect detection system of claim 5, wherein the non-standard camera mount comprises:
a first circular table, which is positioned at the top,
the second circular table is positioned on the lower layer of the first circular table, the second circular table and the first circular table are mutually supported and fixed by at least one upright post, and the industrial camera is arranged at the center of the second circular table;
the annular light source is positioned on the lower layer of the second circular truncated cone, and the annular light source and the lower layer of the second circular truncated cone are mutually supported and fixed by at least one support column.
7. The machine vision-based weld spot defect detecting system of claim 1, wherein the detecting whether the body-in-white reaches the work station comprises:
the control module confirms whether the body-in-white arrives at a designated station according to the working state of the rolling machine;
the control module issues a moving instruction to the robot module, so that the camera reaches a specified position, and a light source is started to collect an image;
the vision algorithm module identifies the vehicle body and confirms whether the vehicle body meets the requirement of detection pose;
and if the pose meets the detection requirement, confirming that the white vehicle body station is confirmed.
8. A welding spot defect detection method based on machine vision is characterized in that:
collecting image data;
processing the image data to perform welding spot identification;
and detecting the identified welding spots for defects.
9. The method for detecting welding spot defects based on machine vision according to claim 8, characterized in that: the processing of the image data for solder joint recognition includes:
preprocessing the image data with median filtering and edge sharpening,
the weld point edges in the preprocessed image are extracted by canny filtering,
the welding points are detected by utilizing Hough transform,
and screening holes and false welding spots by combining the gray value of the area where the welding spot is located and the neighborhood condition of the outline of the area, and obtaining the identified welding spot.
10. The method for detecting welding spot defects based on machine vision according to claim 8, characterized in that: detecting the identified welding spot defects, comprising:
extracting shape features and gray scale features of the identified welding spots;
calculating the position information and the shape information of the welding spot based on the shape characteristic and the gray characteristic, and obtaining a calculation result;
and judging whether defects exist according to the calculation result.
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